Sparse Range Image Compression for LiDAR Point Clouds
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Solution Overview
Problem
Existing point cloud compression techniques for LiDAR-generated sparse point clouds do not achieve a reasonable balance between compression efficiency and computational complexity, particularly with methods like G-PCC, which may not provide sufficient efficiency for computational demands.
Innovation Solution
A method involving conversion of a 3D point cloud into a 2D sparse range image (SRI) and an occupancy map, followed by packing and filling operations to create a packed SRI, and encoding these into sub-bitstreams for efficient compression, utilizing loss-less coding and AI-based techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If G-PCC (octree-based approach) is used for compression, then sparse point cloud compression is achieved, but compression efficiency is insufficient for computational demands
Solution Approach 1:
The patent transforms the 3D point cloud into a 2D sparse range image through projection, adding a dimensional transformation step. This allows the use of efficient 2D image compression techniques (like JPEG 2000 or AI-based methods) to compress the point cloud data, achieving higher compression efficiency while reducing the computational complexity compared to traditional 3D-specific methods like G-PCC octree approach
2Quantity of substance
If point cloud data is compressed to reduce size, then data integrity may be compromised, but lossless coding maintains accuracy
Solution Approach 1:
The patent segments the point cloud compression into multiple independent components: occupancy map encoding and sparse range image encoding. The occupancy map uses lossless coding to preserve structural information, while the sparse range image can use lossy or lossless coding depending on requirements. This segmentation allows flexible control over compression vs. fidelity trade-offs
Solution Approach 2:
The patent introduces an occupancy map as an intermediary structure between the 3D point cloud and the compressed representation. The occupancy map serves as a lossless mask that indicates which regions contain point cloud data, enabling the system to compress only the essential information while preserving data integrity through the occupancy guidance
Data Source
AI summary
A bitstream including coded information of a three-dimensional (3D) point cloud is received. The 3D point cloud includes a plurality of points in a 3D space. The bitstream is parsed into a first sub-bitstream associated with an occupancy map and a second sub-bitstream associated with a sparse range image (SRI) in a two-dimensional (2D) space. The SRI is converted from the 3D point cloud in the 3D space. The occupancy map indicates whether one of a plurality of samples of the SRI has a corresponding point in the 3D point cloud. The occupancy map is determined based on the first sub-bitstream and the SRI based on the second sub-bitstream. The 3D point cloud is reconstructed based on the SRI and the occupancy map.